Search Results (1,230)

This study proposes two optimization models and a column-generation algorithm, applied at the root node, to support tactical planning in sawmills by dynamically generating log cutting patterns aligned with sustainability and efficiency objectives. Starting from an industrial dataset containing 160 cutting patterns, the methodology iteratively incorporates new geometrically feasible configurations guided by the dual prices of a primary model, explicitly considering log supply, product demand, and alternative tactical criteria. Three computational experiments were conducted. The first assesses the convergence behavior of the algorithm and shows reductions in total log consumption of up to 31% as new patterns are generated. The second demonstrates that strategies aimed at minimizing log usage and residues can achieve near-optimal solutions with only 20–25 patterns, since additional configurations provide marginal improvements while increasing setup time and operational complexity. The third experiment confirms that near-optimal performance can be reached with a moderate number of active patterns, facilitating practical implementation in industrial settings. Overall, the proposed methodology offers a flexible and sustainability-oriented decision-support tool for sawmill tactical planning, improving raw-material utilization, reducing residues, and enhancing alignment between supply and demand while maintaining operational feasibility. Full article

Lysosomal storage disorders (LSDs) are a group of rare inherited diseases caused by mutations in the genes encoding the proteins involved in normal lysosomal functions, leading to an accumulation of undegraded substrates within lysosomes. Among the most prominent clinical features are neurological impairment and neurodegeneration, arising from widespread cellular dysfunction. The development of powerful and reliable animal model systems that can in vivo recapitulate human LSD pathologies is critical for understanding disease mechanisms and advancing therapeutic strategies. In this study, we identified the Drosophila melanogaster orthologs of human LSD-related genes using the DIOPT tool and performed tissue-specific gene silencing along the brain–midgut axis via the use of GAL4/UAS and RNAi combined technologies. Transgenic fly models presented key features of human LSD pathologies, including significantly shortened lifespans and a progressive locomotor decline that serves as a measure for neuromuscular disintegration, following age- and sex-dependent patterns. These phenotypic parallels in pathology strongly support the functional relevance of the selected orthologs and underscore the value of Drosophila as a versatile in vivo model system for advanced LSD pathology research, offering state-of-the-art genetic tools for molecularly dissecting disease mechanisms and providing cutting-edge novel platforms for high-throughput genetic and/or pharmacological screening, moving towards development of new therapeutically beneficial drug-based regimens and mutant gene-rescue schemes. Full article

Airborne magnetic particles (AMPs) are associated with potentially toxic elements, and their size, mineralogy, and concentration can significantly impact both the environment and human health. However, their spatial analysis is often limited by small datasets, non-normality, and pronounced local variability. In this work, two sites with distinct demographic and geographic characteristics, the city of Mar del Plata (Argentina) and the Aburrá Valley region (Colombia), were analyzed using the fuzzy Magnetic Pollution Index (IMC) as an indicator of the concentration of AMPs. Moreover, an original methodological framework that explicitly incorporates measurement uncertainty through fuzzy numbers, combined with an approach modeling fuzzy semivariances via α-cuts, performs spatial prediction via ordinary kriging. This study produces maps that simultaneously reflect the magnitude of IMC and its associated uncertainty. Unlike classical geostatistics, the fuzzy-based model captures the inherent imprecision of magnetic measurements and reveals spatial patterns where uncertainty becomes informative about the type and origin of pollution. In particular, this approach demonstrates that areas with higher IMC levels are associated with high anthropic activity (near industrial zones, main avenues, slow traffic). In contrast, lower values were found in residential areas. Overall, the fuzzy-driven approach provides an additional layer of information not accessible through traditional methods, improving spatial interpretation and supporting the identification of priority areas for environmental monitoring. Full article

In the Ordos Basin, the lengths of cores are disproportionate to image logging data (1:9) and fracture research is difficult because of their complex lithology and fracture patterns. Based on the characteristics of conventional logging and cores, this paper describes the color, shape, geophysical characteristics and geological features of the basin to establish an image recognition template and to identify nine distinct lithologies. The genesis, type, occurrence, opening mode, cutting depth, host lithology, density and tectonic stress of the fractures are used to define four types of fracture facies (bedding fracture facies, N100° tectonic fracture facies, N10° tectonic fracture facies and coal fracture facies) and to build four models. The comprehensive coherence among the neural network results, curvatures, ant bodies, lithologies, and thicknesses was used to predict the type of different fracture facies. The results show that the fracture prediction model fully reflects the genesis of the cracks and influencing factors and provides insights into optimal areas for future exploration and development. Full article

High and variable moisture in wood logs limits their use in continuous carbonization reactors. Artificial drying emerges as a solution to homogenize the moisture of the raw material, optimizing the process, increasing yield, and improving the quality of charcoal. This study aimed to develop an experimental fixed-bed drying system for logs, evaluating the effects of cutting layout (40 cm, 20 cm, and split), diameter class (>12 cm, 12.1–14 cm, 14.1–16 cm, and 16.1–18 cm), and residence time (30, 60, and 90 min) at 300 °C. Split logs showed higher heating and drying rates, positively impacting efficiency. However, split and 20 cm logs subjected to 90 min of drying underwent combustion, indicating operational limits for these layouts under the tested conditions. The heartwood and sapwood regions of split logs heated more rapidly, resulting in higher drying rates and moisture loss, directly affecting drying efficiency. Split logs dried for 60 min showed the best drying efficiency and greatest moisture reduction, making this the most recommended treatment. This study not only demonstrates the technical feasibility of artificial drying of logs for continuous carbonization but also establishes fundamental guidelines for the development of more efficient, safe and sustainable industrial technologies in the charcoal production sector. Full article

(1) Background: Female homelessness is one of the most invisible forms of social exclusion, aggravated by structural and gender factors and by experiences of violence. This research analyzes the multifaceted barriers hindering women experiencing homelessness from accessing healthcare services, from the perspective of social intervention professionals. (2) Methods: A qualitative study was conducted using three focus groups with 21 professionals from Santa Cruz de Tenerife, Lleida and Barcelona. An interpretative phenomenological approach guided data collection and analysis, and transcripts were examined through thematic analysis to identify common patterns in professionals’ meaning-making regarding healthcare barriers. (3) Results: Gender-based violence cuts across the life trajectories of most women experiencing homelessness, hindering their access to healthcare services. Barriers identified include lack of documentation, stigma and discriminatory treatment, limited access to specialized services, the absence of a gender perspective in healthcare, and a lack of coordination between social and health services. In addition, the study highlights the lack of preventive programs and health education tailored to this population. (4) Conclusions: It is essential to adopt a comprehensive, intersectional and gender-sensitive approach to safeguard the right to health for these women. Measures such as training for healthcare personnel, simplifying bureaucratic procedures, creating specialized resources, and improving inter-institutional coordination are proposed. Full article

Biochar has gained significant attention as a multifunctional material linking biomass energy technologies with sustainable agriculture, providing combined benefits in soil improvement, waste valorization, and climate mitigation. This review examines biochar within the context of thermochemical conversion processes—pyrolysis, gasification, and torrefaction—and summarizes the operational parameters that influence both energy yields and biochar quality. It synthesizes agronomic, environmental, and engineering research to explain the mechanisms through which biochar enhances soil structure, nutrient retention, water availability, microbial activity, and carbon stability. The review also assesses its role as a long-term carbon sink and its potential integration into negative-emission systems such as bioenergy with carbon capture and storage (BECCS). However, the way that biomass conversion factors concurrently influence energy performance, biochar physicochemical quality, and its agronomic and climate-mitigation consequences across many environmental contexts is rarely integrated into a unified analytical framework in current evaluations. To close that gap, this review identifies cross-cutting patterns, trade-offs, and uncertainties while methodically integrating the information on the co-behavior of various aspects. Circular economy initiatives, carbon markets, and rural development are mentioned as key potential. On the other hand, economic variability, variable performance across soil types, lack of regulatory harmonization, rivalry for biomass, and logistical limits are big hurdles. Standardized production techniques, long-term field research, life cycle and techno-economic evaluations, and integrated system design are among the top research priorities. Overall, the evidence suggests that biochar is a promising tool for creating resilient and low-carbon agriculture and energy systems, provided that scientific, technological, and governance advancements are coordinated. Full article

Background/Objectives: Tuberculosis remains a significant global health problem, particularly in resource-limited environments. Its mortality and spread can be considerably decreased by early and precise detection via chest X-ray imaging. This study introduces a novel approach based on hybrid deep learning for Tuberculosis detection from chest X-ray images. Methods: The introduced approach combines GhostNet, a lightweight convolutional neural network tuned for computational efficiency, and MobileViT, a transformer-based model that can capture both local spatial patterns and global contextual dependencies. Through such integration, the model attains a balanced trade-off between classification accuracy and computational efficiency. The architecture employs feature fusion, where spatial features from GhostNet and contextual representations from MobileViT are globally pooled and concatenated, which allows the model to learn discriminative and robust feature representations. Results: The suggested model was assessed on two publicly available chest X-ray datasets and contrasted against several cutting-edge convolutional neural network architectures. Findings showed that the introduced hybrid model surpasses individual baselines, attaining 99.52% accuracy on dataset 1 and 99.17% on dataset 2, while keeping low computational cost (7.73M parameters, 282.11M Floating Point Operations). Conclusions: These outcomes verify the efficacy of feature-level fusion between a convolutional neural network and transformer branches, allowing robust tuberculosis detection with low inference overhead. The model is ideal for clinical deployment and resource-constrained contexts due to its high accuracy and lightweight design. Full article

For advanced semiconductor nodes, the demand for high-precision patterning of complex foundry circuits drives the widespread use of Lithography-Etch-Lithography-Etch (LELE)—a key Multiple Patterning Technology (MPT)—in Deep Ultraviolet (DUV) processes. However, the interaction between LELE’s two Lithography-Etch (LE) cycles makes it very challenging to build a model for etching contour simulation and hotspot detection. This study presents an Artificial Intelligence (AI)-assisted composite etch model to capture inter-LE interactions, which directly outputs the final post-LELE etch contour, enabling Etch Rule Check (ERC)-based simulation detection of After Etch Inspection (AEI) hotspots. In addition, the etch model proposed in this study can also predict the etch bias of different types of pattern (especially complex two-dimensional (2D) patterns), thereby enabling auto retargeting for After Develop Inspection (ADI) target generation. In the future, the framework of this composite model can be adapted to the Self-Aligned Reverse Patterning (SARP) + Cut process to address more complex MPT challenges. Full article

As global food demand grows, the limited availability of natural resources exacerbates environmental and food security challenges. Household food waste is a major yet underexplored issue, contributing to inefficiencies, economic losses, and environmental harm. This study applies the Environmental-Economic Footprint (EN-EC) index to assess household food waste in Ireland. By integrating environmental and economic data, this index facilitates a comprehensive dual-perspective evaluation of food waste impacts. Data were collected from 1000 Irish households, analyzing waste patterns across 12 food categories. Environmental impacts were quantified using global warming potential (GWP) and water footprint (WF), while economic costs were based on waste generation and disposal. The EN-EC index synthesizes these parameters to facilitate informed decision-making. On average, Irish households reported approximately 966 g (0.97 kg) of edible food waste per week, equivalent to around 50 kg annually per household. This amount results in substantial associated impacts, including greenhouse gas emissions and water consumption, quantified through literature-based footprint coefficients. Red meat, particularly beef, contributes disproportionately to environmental and economic burdens despite its relatively lower waste volume. A 50% reduction in meat waste could cut CO₂ emissions by 2.5 kg, water use by 563.50 L, and costs by €3623.48. These insights equip policymakers with targeted strategies to mitigate food waste, aligning with global sustainability goals. Full article

The accurate modelling of material removal mechanisms in grinding processes requires precise constitutive equations describing dynamic material behaviour under extreme strain rates and large deformations. This study presents a novel methodology for optimising the Johnson–Cook (J–C) constitutive model parameters for micro-grinding applications, addressing the limitations of conventional mechanical testing at strain rates exceeding 10⁵ s⁻¹. The research employed single abrasive grain micro-cutting experiments using a diamond Vickers indenter on aluminium alloy 7075-T6 specimens. High-resolution topographic measurements (130 nm lateral resolution) were used to analyse the scratch geometry and lateral material displacement patterns. Ten modified J–C model variants (A1–A10) were systematically evaluated through finite element simulations, focusing on parameters governing plastic strengthening (B, n) and strain rate sensitivity (C). Quantitative non-conformity criteria assessed agreement between experimental and simulated results for cross-sectional areas and geometric shapes of material pile-ups and grooves. These criteria enable an objective evaluation by comparing the pile-up height (h), width (l), and horizontal distance to the peak (d). The results demonstrate that conventional J–C parameters from Hopkinson bar testing exhibit significant discrepancies in grinding conditions, with unrealistic stress values (17,000 MPa). The optimised model A3 (A = 473 MPa, B = 80 MPa, n = 0.5, C = 0.001) achieved superior convergence, reducing the non-conformity criteria to Σk_A = 0.46 and Σk_K = 1.16, compared to 0.88 and 1.67 for the baseline model. Strain mapping revealed deformation values from ε = 0.8 to ε = 11 in lateral pile-up regions, confirming the necessity of constitutive models describing material behaviour across wide strain ranges. The methodology successfully identified optimal parameter combinations, with convergence errors of 1–14% and 7–60% on the left and right scratch sides, respectively. The approach provides a cost-effective alternative to expensive dynamic testing methods, with applicability extending to other ductile materials in precision manufacturing. Full article

This study investigates vibration signals generated during end milling of thin-walled EN AW-7075 aluminum alloy components using a set of 24 tools with distinct cutting edge microgeometries. Five characteristic parameters describing the dynamic response of the process, including both energy-related and statistical indicators, were extracted and analyzed. The results clearly demonstrate the critical influence of tool microgeometry on process dynamics. In particular, the introduction of an additional zero-clearance flank land at the cutting edge proved decisive in suppressing vibrations. For the most favorable geometries, the root mean square (RMS) value of vibration was reduced by more than 50%, while the spectral power density (PSD) decreased by up to 70–75% compared with the least favorable configurations. Simultaneously, both time- and frequency-domain responses exhibited complex and irregular patterns, highlighting the limitations of intuitive interpretation and the need for multi-parameter evaluation. To enable a synthetic comparison of tools, the Vibration Severity Index (VSI), which integrates RMS and kurtosis into a single composite metric, was introduced. VSI-based ranking allowed the clear identification of the most dynamically stable geometry. For the selected tool, additional analysis was conducted to evaluate the influence of cutting parameters, namely feed per tooth and radial depth of cut. The results showed that the most favorable dynamic behavior was achieved at a feed of 0.08 mm/tooth and a radial depth of cut of 1.0 mm, whereas boundary conditions resulted in higher kurtosis and a more impulsive signal structure. Overall, the findings confirm that properly engineered cutting-edge microgeometry, especially the formation of additional zero-clearance flank land significantly enhances the dynamic of thin-wall milling, demonstrating its potential as an effective strategy for vibration suppression and process optimization in precision machining of lightweight structural materials. Full article

To address the pressing challenges of low new energy utilization, high power system operating costs, and compromised power supply reliability in regional grids, we propose a multi-time-scale source–storage–load coordinated scheduling strategy that explicitly accounts for the characteristic distribution of grid-connected energy storage stations, including their state-of-charge constraints, round-trip efficiency profiles, and location-specific operational dynamics. A day-ahead scheduling framework is developed by integrating the multi-time-scale behavioral patterns of diverse load-side demand response resources with the dynamic operational characteristics of energy storage stations. By embedding intra-day rolling optimization and real-time corrective adjustments, we mitigate prediction errors and adapt to unforeseen system disturbances, ensuring enhanced operational accuracy. The objective function minimizes a weighted sum of system operation costs encompassing generation, transmission, and auxiliary services; wind power curtailment penalties for unused renewables; and load shedding penalties from unmet demand, balancing economic efficiency with supply quality. A mixed-integer programming model formalizes these tradeoffs, solved via MATLAB 2020b coupled CPLEX to guarantee optimality. Simulation results demonstrate that the strategy significantly cuts wind power curtailment, reduces system costs, and elevates new energy consumption—outperforming conventional single-time-scale methods in harmonizing renewable integration with grid reliability. This work offers a practical solution for enhancing grid flexibility in high-renewable penetration scenarios. Full article

Branched horizontal wells are widely applied in oil and gas development. However, their complex structures make cuttings transport and deposition problems more pronounced. In this study, a three-dimensional branched wellbore model was established based on an intelligent drilling and completion simulation system. A computational fluid dynamics (CFD) approach, incorporating the Eulerian–Eulerian two-fluid model and the kinetic theory of granular flow, was employed to investigate the effects of wellbore diameter, eccentricity, curvature, flow rate, and rheological parameters on cuttings transport behavior. Results from the steady-state simulations indicate that increasing the wellbore diameter and eccentricity intensifies cuttings deposition at the connection section, with the lower-region concentration rising significantly as the eccentricity increases from 0% to 60%. A larger curvature enhances local flow disturbance but reduces the overall cuttings transport efficiency. Increasing the flow rate improves hole cleaning but may promote cuttings accumulation near the bottom of the main wellbore. As the flow behavior index increases from 0.4 to 0.8, the average cuttings concentration rises from 0.0996 to 0.1008, and the pressure drop increases from 1,010,894 Pa to 1,042,880 Pa, indicating improved transport capacity but higher energy consumption. Experimental results are consistent with the numerical simulation trends, confirming the model’s reliability. This study provides both theoretical and experimental support for optimizing complex wellbore structures and drilling fluid parameters. Full article

In applications involving repeated high-frequency mechanical impacts, such as cutting, machining, or percussive operations, understanding the surface fatigue performance of advanced ceramics is critical. This study investigated the surface fatigue resistance of duplex oxide–carbide ceramic composites fabricated via spark plasma sintering, complementing prior work on their sliding wear performance. The composites, featuring a hybrid oxide–carbide structure, were tested using a cyclic impact setup with a 10 mm ZrO₂ ball activated with 12 hammers fixed to a rotary disc delivering 500,000 impacts per test. Surface degradation was quantified through three-dimensional profilometry to determine the net material loss and scar depth, while fatigue mechanisms were analyzed using scanning electron microscopy coupled with energy-dispersive spectroscopy. In situ monitoring was implemented using accelerometers to capture vibrational signatures during cycling loading, enabling real-time assessment of material response and damage evolution. The WC-containing composite (S2 AZW) exhibited the lowest surface fatigue wear loss (700 × 10³ µm³), whereas the ZrC-based composite (AZZ1) showed the highest (1535 × 10³ µm³). A distinct inverse correlation was observed between the average peak acceleration and fatigue wear loss. Frequency-domain analysis of accelerometric signals revealed progressive degradation patterns consistent with post-test surface damage, indicating that such signal features may serve as effective in situ indicators for tracking material fatigue in future applications. Full article